PT  - JOURNAL ARTICLE
AU  - Edward Mountjoy
AU  - Ellen M. Schmidt
AU  - Miguel Carmona
AU  - Gareth Peat
AU  - Alfredo Miranda
AU  - Luca Fumis
AU  - James Hayhurst
AU  - Annalisa Buniello
AU  - Jeremy Schwartzentruber
AU  - Mohd Anisul Karim
AU  - Daniel Wright
AU  - Andrew Hercules
AU  - Eliseo Papa
AU  - Eric Fauman
AU  - Jeffrey C. Barrett
AU  - John A. Todd
AU  - David Ochoa
AU  - Ian Dunham
AU  - Maya Ghoussaini
TI  - Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published GWAS trait-associated loci
AID  - 10.1101/2020.09.16.299271
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.09.16.299271
4099  - http://biorxiv.org/content/early/2020/09/17/2020.09.16.299271.short
4100  - http://biorxiv.org/content/early/2020/09/17/2020.09.16.299271.full
AB  - Genome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.Competing Interest StatementThe authors have declared no competing interest.